Dynamic modeling of human error in industrial maintenance through structural analysis and system dynamics.

Human error constitutes a significant cause of accidents across diverse industries, leading to adverse consequences and heightened disruptions in maintenance operations. Organizations can enhance their decision-making process by quantifying human errors and identifying the underlying influencing factors, thereby mitigating their repercussions. Consequently, it becomes crucial to examine the value of human error probability (HEP) during these activities. The objective of this paper is to determine and simulate HEP in maintenance tasks at a cement factory, utilizing performance shaping factors (PSFs). The research employs the cross-impact matrix multiplication applied to classification (MICMAC) analysis method to evaluate the dependencies, impacts, and relationships among the factors influencing human error. This approach classifies and assesses the dependencies and impacts of different factors on HEP, occupational accidents, and related costs. The study also underscores that PSFs can dynamically change under the influence of other variables, emphasizing the necessity to forecast the behavior of human error over time. Therefore, this paper utilizes the MICMAC method to analyze the interdependencies, relationships, and impact levels among different variables. These relationships are then utilized to optimize the implementation of the system dynamics (SD) method. An SD model is employed to forecast the system's behavior, and multiple scenarios are presented. By considering the HEP value, managers can adjust organizational conditions and personnel to ensure acceptability. The paper also presents various scenarios related to HEP to assist managers in making informed decisions.

The Potential of Using Generative AI/NLP to Identify and Analyse Critical Incidents in a Critical Incident Reporting System (CIRS): A Feasibility Case-Control Study.

BACKGROUND: To enhance patient safety in healthcare, it is crucial to address the underreporting of issues in Critical Incident Reporting Systems (CIRSs). This study aims to evaluate the effectiveness of generative Artificial Intelligence and Natural Language Processing (AI/NLP) in reviewing CIRS cases by comparing its performance with human reviewers and categorising these cases into relevant topics. METHODS: A case-control feasibility study was conducted using CIRS cases from the German CIRS-Anaesthesiology subsystem. Each case was reviewed by a human expert and by an AI/NLP model (ChatGPT-3.5). Two CIRS experts blindly assessed these reviews, rating them on linguistic quality, recognisable expertise, logical derivability, and overall quality using six-point Likert scales. RESULTS: On average, the CIRS experts correctly classified 80% of human CIRS reviews as created by a human and misclassified 45.8% of AI reviews as written by a human. Ratings on a scale of 1 (very good) to 6 (failed) revealed a comparable performance between human- and AI-generated reviews across the dimensions of linguistic expression (p = 0.39), recognisable expertise (p = 0.89), logical derivability (p = 0.84), and overall quality (p = 0.87). The AI model was able to categorise the cases into relevant topics independently. CONCLUSIONS: This feasibility study demonstrates the potential of generative AI/NLP in analysing and categorising cases from the CIRS. This could have implications for improving incident reporting in healthcare. Therefore, additional research is required to verify and expand upon these discoveries.

Analysis of human errors in the operation of various treatment planning systems over a 10-year period.

The present study aimed to summarize and report data on errors related to treatment planning, which were collected by medical physicists. The following analyses were performed based on the 10-year error report data: (1) listing of high-risk errors that occurred and (2) the relationship between the number of treatments and error rates, (3) usefulness of the Automated Plan Checking System (APCS) with the Eclipse Scripting Application Programming Interface and (4) the relationship between human factors and error rates. Differences in error rates were observed before and after the use of APCS. APCS reduced the error rate by ~1% for high-risk errors and 3% for low-risk errors. The number of treatments was negatively correlated with error rates. Therefore, we examined the relationship between the workload of medical physicists and error occurrence and revealed that a very large workload may contribute to overlooking errors. Meanwhile, an increase in the number of medical physicists may lead to the detection of more errors. The number of errors was correlated with the number of physicians with less clinical experience; the error rates were higher when there were more physicians with less experience. This is likely due to the lack of training among clinically inexperienced physicians. An environment to provide adequate training is important, as inexperience in clinical practice can easily and directly lead to the occurrence of errors. In any environment, the need for additional plan checkers is an essential factor for eliminating errors.

Prediction of human error probability in helicopter to ship transfer operation under an evidential reasoning extended CREAM approach.

Although the helicopter to ship transfer operation is rarely applied in ports around the world, some of the hub ports have encouraged it. The operational process poses challenges to safety due to the nature of the work to be completed by the ship crew. This article performs a systematic human error prediction to ensure safe helicopter to ship transfer operations under different circumstances since the human factor is becoming a significant contributor to maritime accidents. To accomplish this, the article addresses a cognitive reliability and error analysis method (CREAM) under the evidential reasoning (ER) approach. In the proposed approach, the CREAM provides a comprehensive human error prediction tool and ER is capable of supporting experts' judgment in decision-making. The findings of the research show that 'Secure the loose objects within or adjacent to the operating area' poses a high human error probability (4.50E-02) during helicopter to ship transfer operations.

A generic approach to developing human factors-quality assessment tools exemplified by the warehouse error prevention tool.

This study proposes a generic approach for creating human factors-based assessment tools to enhance operational system quality by reducing errors. The approach was driven by experiences and lessons learned in creating the warehouse error prevention (WEP) tool and other system engineering tools. The generic approach consists of 1) identifying tool objectives, 2) identifying system failure modes, 3) specifying design-related quality risk factors for each failure mode, 4) designing the tool, 5) conducting user evaluations, and 6) validating the tool. The WEP tool exemplifies this approach and identifies human factors related to design flaws associated with quality risk factors in warehouse operations. The WEP tool can be used at the initial stage of design or later for process improvement and training. While this process can be adapted for various contexts, further study is necessary to support the teams in creating tools to identify design-related human factors contributing to quality issues.

This paper describes a generic approach to creating human factors­based quality assessment tools. The approach is illustrated with the Warehouse Error Prevention (WEP) tool, which is designed to help users identify HF-related quality risk factors in warehouse system designs (available for free: Setayesh et al. 2022b).

Human error identification and risk assessment in loading and unloading of petroleum products by road trucks using the SHERPA and fuzzy inference system method.

Human error constitutes one of the primary causes of accidents, particularly in the context of loading and unloading operations involving road trucks, especially those carrying petroleum products. The process of identifying and evaluating human errors within these operations involves several key steps. Initially, all sub-tasks associated with loading and unloading are meticulously identified and analyzed utilizing Hierarchical Task Analysis (HTA), achieved through direct observation, document examination, and interviews. Subsequently, potential human error modes within each task are delineated using the Systematic Human Error Reduction and Prediction Approach (SHERPA). Finally, essential data for determining the criticality, probability, and severity of each error are gathered through expert elicitation and the application of Fuzzy Inference Systems (FIS). Through the analysis of SHERPA worksheets, a total of 37 errors during loading operations and 14 errors during unloading operations of petroleum products were identified. Among these errors, the predominant category during loading operations was action errors, comprising 31 instances, while communication errors were the least frequent, occurring only twice. Similarly, action errors were most prevalent during unloading operations, constituting 13 instances. These errors were further categorized and ranked based on their risk levels, resulting in 27 levels for loading operations and 12 levels for unloading operations. The consistent occurrence of action errors underscores the need for implementing control measures to mitigate their frequency and severity. Such strategies may include periodic training sessions to reinforce proper work procedures and the development of monitoring checklists, among other interventions.

Human errors in emergency medical services: a qualitative analysis of contributing factors.

BACKGROUND: The dynamic and challenging work environment of the prehospital emergency care settings creates many challenges for paramedics. Previous studies have examined adverse events and patient safety activities, but studies focusing on paramedics' perspectives of factors contributing to human error are lacking. In this study, we investigated paramedics' opinions of the factors contributing to human errors. METHOD: Data was collected through semi-structured individual interviews (n = 15) with paramedics and emergency medical field supervisors in Finland. The data was analyzed using inductive content analysis. Consolidated criteria for reporting qualitative research were used. RESULTS: Contributing factors to human errors were divided into three main categories. The first main category, Changing work environment, consisted of two generic categories: The nature of the work and Factors linked to missions. The second main category, Organization of work, was divided into three generic categories: Inadequate care guidelines, Interaction challenges and Challenges related to technological systems. The third main category, Paramedics themselves, consisted of four generic categories: Issues that complicate cognitive processing, Individual strains and needs, Attitude problems and Impact of work experience. CONCLUSION: Various factors contributing to human errors in emergency medical services (EMS) settings were identified. Although many of them were related to individual factors or to the paramedics themselves, system-level factors were also found to affect paramedics' work and may therefore negatively impact patient safety. The findings provide insights for organizations to use this knowledge proactively to develop their procedures and to improve patient safety.

High-speed train drivers' human error under fatigue and stress: the role of situation awareness and individual differences.

Fatigue and stress are critical variables that impair railway train drivers' safety performance, and individual differences may influence these effects. This study investigates how fatigue and stress affect high-speed train drivers' human error and the role of individual differences. We hypothesised that situation awareness (SA) mediates the effects of fatigue and stress on human error, and individual differences (age and work experience) moderate these effects. We surveyed 1,391 male drivers from eight Chinese railway bureaus and used PROCESS Macro for data analysis. The results revealed that fatigue and stress increased human error, directly and indirectly through SA. Age and work experience moderated the effect of fatigue and stress on SA, respectively. Older drivers had better SA under high fatigue, while more experienced drivers had better SA under high stress. These findings can inform more tailored safety management strategies to lower human error and enhance the safety of high-speed train operations.

A cross-sectional survey of 1,391 high-speed train drivers in China indicated that fatigue and stress amplify human error by impairing situation awareness (SA). Age and work experience were observed to moderate the impact of fatigue and stress on SA, respectively. These insights guide the advancement of safety management strategies.

Hybrid electronic record: An error reduction strategy for diverse medical prescription formats.

BACKGROUND:  This project is part of a broader effort to develop a new electronic registry for ophthalmology in the KwaZulu-Natal (KZN) province in South Africa. The registry should include a clinical decision support system that reduces the potential for human error and should be applicable for our diversity of hospitals, whether electronic health record (EHR) or paper-based. METHODS:  Post-operative prescriptions of consecutive cataract surgery discharges were included for 2019 and 2020. Comparisons were facilitated by the four chosen state hospitals in KZN each having a different system for prescribing medications: Electronic, tick sheet, ink stamp and handwritten health records. Error types were compared to hospital systems to identify easily-correctable errors. Potential error remedies were sought by a four-step process. RESULTS:  There were 1307 individual errors in 1661 prescriptions, categorised into 20 error types. Increasing levels of technology did not decrease error rates but did decrease the variety of error types. High technology scripts had the most errors but when easily correctable errors were removed, EHRs had the lowest error rates and handwritten the highest. CONCLUSION:  Increasing technology, by itself, does not seem to reduce prescription error. Technology does, however, seem to decrease the variability of potential error types, which make many of the errors simpler to correct.Contribution: Regular audits are an effective tool to greatly reduce prescription errors, and the higher the technology level, the more effective these audit interventions become. This advantage can be transferred to paper-based notes by utilising a hybrid electronic registry to print the formal medical record.

Using SHERPA to predict human error on the maritime SAR helicopter hoist task.

In the realm of air-maritime SAR missions, technical errors are relatively rare compared to human errors due to the multifaceted nature of these missions that standard checklists may not fully encompass. Thus, prioritizing pilot training and implementing a systematic approach are vital to mitigate pilot errors in SAR missions. To mitigate and predict human errors during maritime SAR helicopter hoist tasks, SHERPA methodology is applied in this study. This analysis uncovered a comprehensive total of 54 potential errors, most applicable to countries utilizing rescue aircraft similar to those in Taiwan. The errors identified in this analysis suggest opportunities for enhancing the design of maritime SAR helicopter hoisting tasks through the application of SHERPA, with the potential to decrease their occurrence in the future.

Identifying and preventing human error in the sugar production process: A multi-stage approach using HTA, HEC and PHEA techniques.

This article discusses the importance of identifying and preventing human error in industrial environments, specifically in the sugar production process. The article emphasizes the importance of choosing the right technique for risk assessment studies resulting from human errors. A cross-sectional study was conducted using a multi-stage approach - Hierarchical Task Analysis (HTA), Human Error Calculator (HEC), and Predictive Human Error Analysis (PHEA) - to identify potential human errors in the sugar production process. The HTA, HEC, and PHEA techniques were employed to evaluate each stage of the process for potential human errors. The results of the HTA technique identified 35 tasks and 83 sub-tasks in 14 units of the sugar production process. According to HEC technique 4 tasks with 80 % probability of human error and 2 tasks with 50 % probability of human error had the highest calculated error probabilities. The factors of individual skill, task repetition and importance were the most important factors of human error in the present study. The analysis of PHEA worksheets showed that the number of human errors identified in the tasks with highest probability were 8 errors, of which 50 % were action errors, 25 % checking errors, 13 % selection errors, and 12 % retrieval errors. To mitigate the consequences of human error, it was recommended training courses, raising operator awareness of error consequences, and installing instructions in the sugar production process. Based on the findings, the article concludes that the HEC and PHEA techniques are applicable and effective in identifying and analyzing human errors in process and food industries.

Interdisciplinary videoconference model for identifying potential adverse transition of care events following hospital discharge to postacute care.

Discharge from hospitals to postacute care settings is a vulnerable time for many older adults, when they may be at increased risk for errors occurring in their care. We developed the Extension for Community Healthcare Outcomes-Care Transitions (ECHO-CT) programme in an effort to mitigate these risks through a mulitdisciplinary, educational, case-based teleconference between hospital and skilled nursing facility providers. The programme was implemented in both academic and community hospitals. Through weekly sessions, patients discharged from the hospital were discussed, clinical concerns addressed, errors in care identified and plans were made for remediation. A total of 1432 discussions occurred for 1326 patients. The aim of this study was to identify errors occurring in the postdischarge period and factors that predict an increased risk of experiencing an error. In 435 discussions, an issue was identified that required further discussion (known as a transition of care event), and the majority of these were related to medications. In 14.7% of all discussions, a medical error, defined as 'any preventable event that may cause or lead to inappropriate medical care or patient harm', was identified. We found that errors were more likely to occur for patients discharged from surgical services or the emergency department (as compared with medical services) and were less likely to occur for patients who were discharged in the morning. This study shows that a number of errors may be detected in the postdischarge period, and the ECHO-CT programme provides a mechanism for identifying and mitigating these events. Furthermore, it suggests that discharging service and time of day may be associated with risk of error in the discharge period, thereby suggesting potential areas of focus for future interventions.

The precision of drill calibration for dynamic navigation.

OBJECTIVES: To quantify the reproducibility of the drill calibration process in dynamic navigation guided placement of dental implants and to identify the human factors that could affect the precision of this process in order to improve the overall implant placement accuracy. METHODS: A set of six drills and four implants were calibrated by three operators following the standard calibration process of NaviDent® (ClaroNav Inc.). The reproducibility of the position of each tip of a drill or implant was calculated in relation to the pre-planned implants' entry and apex positions. Intra- and inter-operator reliabilities were reported. The effects of the drill length and shape on the reproducibility of the calibration process were also investigated. The outcome measures for reproducibility were expressed in terms of variability range, average and maximum deviations from the mean distance. RESULTS: A satisfactory inter-rater reproducibility was noted. The precision of the calibration of the tip position in terms of variability range was between 0.3 and 3.7 mm. We noted a tendency towards a higher precision of the calibration process with longer drills. More calibration errors were observed when calibrating long zygomatic implants with non-locking adapters than with pointed drills. Flexible long-pointed drills had low calibration precision that was comparable to the non-flexible short-pointed drills. CONCLUSION: The clinicians should be aware of the calibration error associated with the dynamic navigation placement of dental and zygomatic implants. This should be taken in consideration especially for long implants, short drills, and long drills that have some degree of flexibility. CLINICAL SIGNIFICANCE: Dynamic navigation procedures are associated with an inherent drill calibration error. The manual stability during the calibration process is crucial in minimising this error. In addition, the clinician must never ignore the prescribed accuracy checking procedures after each calibration process.

Observed rates of surgical instrument errors point to visualization tasks as being a critically vulnerable point in sterile processing and a significant cause of lost chargeable OR minutes.

BACKGROUND: The reporting of surgical instrument errors historically relies on cumbersome, non-automated, human-dependent, data entry into a computer database that is not integrated into the electronic medical record. The limitations of these reporting systems make it difficult to accurately estimate the negative impact of surgical instrument errors on operating room efficiencies. We set out to determine the impact of surgical instrument errors on a two-hospital healthcare campus using independent observers trained in the identification of Surgical Instrument Errors. METHODS: This study was conducted in the 7 pediatric ORs at an academic healthcare campus. Direct observations were conducted over the summer of 2021 in the 7 pediatric ORs by 24 trained student observers during elective OR days. Surgical service line, error type, case type (inpatient or outpatient), and associated length of delay were recorded. RESULTS: There were 236 observed errors affecting 147 individual surgical cases. The three most common errors were Missing+ (n = 160), Broken/poorly functioning instruments (n = 44), and Tray+ (n = 13). Errors arising from failures in visualization (i.e. inspection, identification, function) accounted for 88.6% of all errors (Missing+/Broken/Bioburden). Significantly more inpatient cases (42.73%) had errors than outpatient cases (22.32%) (p = 0.0129). For cases in which data was collected on whether an error caused a delay (103), over 50% of both IP and OP cases experienced a delay. The average length of delays per case was 10.16 min. The annual lost charges in dollars for surgical instrument associated delays in chargeable minutes was estimated to be between $6,751,058.06 and $9,421,590.11. CONCLUSIONS: These data indicate that elimination of surgical instrument errors should be a major target of waste reduction. Most observed errors (88.6%) have to do with failures in the visualization required to identify, determine functionality, detect the presence of bioburden, and assemble instruments into the correct trays. To reduce these errors and associated waste, technological advances in instrument identification, inspection, and assembly will need to be made and applied to the process of sterile processing.

Safety in Teletriage by Nurses and Physicians in the United States and Israel: Narrative Review and Qualitative Study.

BACKGROUND: The safety of telemedicine in general and telephone triage (teletriage) safety in particular have been a focus of concern since the 1970s. Today, telehealth, now subsuming teletriage, has a basic structure and process intended to promote safety. However, inadequate telehealth systems may also compromise patient safety. The COVID-19 pandemic accelerated rapid but uneven telehealth growth, both technologically and professionally. Within 5-10 years, the field will likely be more technologically advanced; however, these advances may still outpace professional standards. The need for an evidence-based system is crucial and urgent. OBJECTIVE: Our aim was to explore ways that developed teletriage systems produce safe outcomes by examining key system components and questioning long-held assumptions. METHODS: We examined safety by performing a narrative review of the literature using key terms concerning patient safety in teletriage. In addition, we conducted system analysis of 2 typical formal systems, physician led and nurse led, in Israel and the United States, respectively, and evaluated those systems' respective approaches to safety. Additionally, we conducted in-depth interviews with representative physicians and 1 nurse using a qualitative approach. RESULTS: The review of literature indicated that research on various aspects of telehealth and teletriage safety is still sparse and of variable quality, producing conflicting and inconsistent results. Researchers, possibly unfamiliar with this complicated field, use an array of poorly defined terms and appear to design studies based on unfounded assumptions. The interviews with health care professionals demonstrated several challenges encountered during teletriage, mainly making diagnosis from a distance, treating unfamiliar patients, a stressful atmosphere, working alone, and technological difficulties. However, they reported using several measures that help them make accurate diagnoses and reasonable decisions, thus keeping patient safety, such as using their expertise and intuition, using structured protocols, and considering nonmedical factors and patient preferences (shared decision-making). CONCLUSIONS: Remote encounters about acute, worrisome symptoms are time sensitive, requiring decision-making under conditions of uncertainty and urgency. Patient safety and safe professional practice are extremely important in the field of teletriage, which has a high potential for error. This underregulated subspecialty lacks adequate development and substantive research on system safety. Research may commingle terminology and widely different, ill-defined groups of decision makers with wide variation in decision-making skills, clinical training, experience, and job qualifications, thereby confounding results. The rapid pace of telehealth's technological growth creates urgency in identifying safe systems to guide developers and clinicians about needed improvements.

The effect of font boldness, noise disturbance and time pressure on human error in the context of cloud change operation.

This study investigated the number of operator errors, task completion time, and workload of subjects at different levels by imposing conditions such as focused text boldness, noise disturbance, and time pressure to simulate a realistic cloud change business process in the laboratory. Results of the study showed that the text bolding of important content reduced the number of errors, whereas noise interference increased the number of errors. Text boldness only reduced the number of corrected errors, and noise interference only increased the number of uncorrected errors. Moreover, bolding was found to have different effects on the number of errors under different noise levels and time pressure levels, with text boldness significantly reducing the number of total errors only in quiet or low time pressure states. Time pressure had no effect on cloud change task error counts, but high time pressure resulted in higher subjective workload.

Operator error is one of the main causes of service failure, and reducing operator error in cloud change operations is of practical importance. In this study, we found focused text boldness could reduce operator errors, while noise could increase the number of errors. High time pressure would lead to a high workload.

Evaluation of human error in oil spill risk in tanker cargo handling operations.

Cargo handling operations on board tankers pose a significant threat to the cleanliness and health of the ocean ecosystem. Incidents originating from these operations are often attributed to human error, as widely acknowledged. Therefore, it is crucial to control the human factor involved in these operations to enhance ship safety and foster a sustainable, clean marine environment. To tackle this problem, this paper presents a novel model that identifies the causal factors behind oil spills resulting from crew failure in these operations. To attain this, fuzzy Bayesian network (FBN) approach is used in this study to analyse the probabilistic correlations among the causal elements that are disclosed qualitatively and quantitatively. Sensitivity analyses and validation procedures are carried out to enhance the accuracy of results. Eliminating errors in cargo calculation is of paramount importance as research has shown that such errors lead to the largest impact on spill during loading and discharging (L&D) operations. The study's findings offer valuable insights into the causes of L&D operation-related spills. Ship management companies, the loss-prevention division of Protection and Indemnity Clubs (P&I), and regulatory bodies may employ the research results to prevent spill repetitions and protect the marine environment.

Using HFACS to understand human error in railway dispatcher performance: a study of proactive safety inspection records.

This study analyzes 4,095 proactive safety inspection records obtained from a large dispatching centre by utilising the HFACS framework. These proactive safety inspection records offer comprehensive documentation of incidents, capturing major accidents and numerous minor discrepancies and lapses that often go unnoticed in accident reports. The analysis revealed that most incidents were attributed to unsafe actions, primarily skill-based errors and poor decision-making. Additionally, contributing factors such as adverse mental states, personal readiness, and crew resource management were found to play a significant role as preconditions for unsafe acts. Path analyses further established a significant correlation between factors such as unsafe supervision, preconditions for unsafe acts, and the occurrence of unsafe acts. In our discussion, we critically evaluate the strengths and limitations of proactive safety inspection records in safety research. Moreover, we emphasise these findings' potential to enhance safety within the railway industry.

Based on a substantial dataset comprising proactive safety inspection records of railway dispatchers rather than the incident reports utilised in prior studies, this paper presents a causal model of human error among railway dispatchers in combination with HFACS and critically evaluates the strengths and limitations of active safety inspection records.